Effects of grammar complexity on artificial grammar learning

Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success.

From surface to structure: exploring implicit learning of Lindenmayer grammars, 2021

Artificial grammars (systems of rules that produce sequences of symbols) are widely used in assessing implicit learning in humans. In this Artificial Grammar Learning (AGL) study, we investigate the relation between linear and hierarchical implicit learning in adults.

Journal of Experimental Psychology: General, 1991

Acta Psychologica, 2002

Participants performed an artificial grammar learning task, in which the standard finite state grammar (J. Verb. Learn. Verb. Behavior 6 (1967) 855) was extended with a recursive rule generating self-embedded sequences. We studied the learnability of such a rule in two experiments. The results verify the general hypothesis that recursivity can be learned in an artificial grammar learning task. However this learning seems to be rather based on recognising chunks than on abstract rule induction. First, performance was better for strings with more than one level of self-embedding in the sequence, uncovering more clearly the self-embedding pattern. Second, the infinite repeatability of the recursive rule application was not spontaneously induced from the training, but it was when an additional cue about this possibility was given. Finally, participants were able to verbalise their knowledge of the fragments making up the sequences--especially in the crucial front and back positions--, whereas knowledge of the underlying structure, to the extent it was acquired, was not articulatable. The results are discussed in relation to previous studies on the implicit learnability of complex and abstract rules. : S 0 0 0 1 -6 9 1 8 ( 0 2 ) 0 0 0 5 7 -4

1992

Reber and Lewis (1977) exposed subjects to a subset of letter strings generated from a synthetic grammar, then asked them to reorder scrambled letters to generate new grammatical strings. The distribution of the frequency of the bigrams composing their solutions correlated better with the frequency of the bigrams composing the full set of strings generated by the grammar than with the frequency of the bigrams composing the subset of strings displayed in the study phase. In his recent overview on implicit learning, Reber (1989) develops this eiperimental result into one of the main supports for his contention that studying grammatical letter strings gives access to the abstract structure of the grammar. However, this result can be accounted for by a set of biases inherent to the Reber and Lewis procedure. In the present experiment, a group of subjects learned from a list of the bigrams making up the study strings, a condition which precludes the abstraction of any high-level rules. The pattern of correlations outlined above also emerged in this condition, thus lending support to our re-interpretation. The notion of implicit learning originates from the field of artificial grammar learning, as explored over the last twenty years by Reber and his associates (e.g. Reber, 1967; Reber, Allen, & Regan, 1985; see Reber, 1989, for a review). In a typical experiment, subjects are first exposed to a set of letter strings generated from a synthetic grammar that defines authorized letters and the permissible transitions between them. Subjects are given no informa-Requests for reprints should be sent to P.

PLoS ONE, 2012

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left-and right-branching structures, as well as between centre-and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning.

Four experiments examined the roles of attentional capacity and type of instructions on the learning of two types of information: artificial grammar structures and rules governing the formation of natural language sentences.

Connectionist models of …, 2001

A significant part of everyday learning occurs incidentally — a process typically described as implicit learning. A central issue in this domain and others, such as language acquisition, is the extent to which performance depends on the acquisition and deployment of abstract rules. Shanks and colleagues have suggested (1) that discrimination between grammatical and ungrammatical instances of a biconditional grammar requires the acquisition and use of abstract rules, and (2) that training conditions — in particular whether instructions orient participants to identify the relevant rules or not — strongly influence the extent to which such rules will be learned. In this paper, we show (1) that a Simple Recurrent Network can in fact, under some conditions, learn a biconditional grammar, (2) that training conditions indeed influence learning in simple auto-associators networks and (3) that such networks can likewise learn about biconditional grammars, albeit to a lesser extent than human participants. These findings suggest that mastering biconditional grammars does not require the acquisition of abstract rules to the extent implied by Shanks and colleagues, and that performance on such material may in fact be based, at least in part, on simple associative learning mechanisms.

Journal of Experimental Psychology: General, 1990

Memory & Cognition, 2011

Artificial grammar learning (AGL) is a widely used experimental paradigm that investigates how syntactic structures are processed. After a familiarization phase, participants have to distinguish strings consistent with a set of grammatical rules from strings that violate these rules. Many experiments report performance solely at a group level and as the total number of correct judgments. This report describes a systematic approach for investigating individual performance and a range of different behaviors. Participants were exposed to strings of the nonfinite grammar A n B n . To distinguish grammatical from ungrammatical strings, participants had to pay attention to local dependencies while comparing the number of stimuli from each class. Individual participants showed substantially different behavioral patterns despite exposure to the same stimuli. The results were replicated across auditory and visual sensory modalities. It is suggested that an analysis that looks at individual differences grants new insights into the processes involved in AGL. It also provides a solid basis from which to investigate sequenceprocessing abilities in special populations, such as patients with neurological lesions.